In general terms, there are only two ways of forming an image. Either every component in an image is formed simultaneously or it is formed sequentially, pixel-by-pixel or voxel by voxel. Visual images formed by the human eye, as well as, with photographic cameras are constructed using three ranges of the visible spectrum, red, green and blue. Multi-spectral and hyper-spectral images are formed using greater numbers of smaller electromagnetic energy ranges, as well as, energies that extend into the infrared spectral range. Multi-spectra are derived from multiple exposures of a scene through several specific ranged band pass filters. Exposing an array of detectors (e.g., CCD chips,) to the illumination reflected from an optical diffraction grating forms hyper-spectra.
There are a number of different approaches to obtain data sets for multi-spectral and hyper-spectral imaging, including: 1) spatial scanning where each two-dimensional sensor output represents a full slit spectrum, 2) spectral scanning where each two-dimensional output represents a monochromatic spectral map, 3) non-scanning where a single two-dimensional sensor output contains all spatial, and 4) spectral data and spatial-spectral scanning where each two dimensional sensor output represents a wavelength-coded spatial map of the scene. In all these cases, the spatial and spectral data can be represented as a three-dimensional structure, referred to as a data cube, where spatial image dimensions are represented on two axes (x, y) and the spectral dimension is represented on a third wavelength axis (λ).
Combining visual data with multi-spectral or hyper-spectral data, into a data cube, has the advantage of including spectral information with every position in the image, e.g., each pixel of a visual digital image. This correlation serves for example to provide chemical identification signatures of objects in the visual images.
Over the past several decades, the application of multi-spectral and hyper-spectral imaging has become increasingly wide spread in fields such as geography, agriculture, forestry, oceanographic and environmental research, forensics, surveillance, medicine, and astronomy. These imaging technologies also help remote identification of material components from aircraft, satellites, and space stations.
In most of the outdoor applications, the sun is used as a source of illumination in performing multi-spectral and hyper-spectral imaging. The analysis of multi-spectra and hyper-spectra are complicated even in the case of a single material mostly due to the spectral components of illuminating light giving rise to absorption, transmission, reflection, and fluorescence originating from the material under study. When performing multi-spectral and hyper-spectral imaging on a heterogeneous field, even greater complexity is introduced into the resulting spectra due to multiple materials. Moreover, the illumination and background spectral components can overwhelm and hide the spectral components of the materials of interest in the imaging field.
A great deal of effort has been focused on corrective modeling to reduce the impact of the illumination and background spectral components. For example, the spectrum of black body radiation that closely models the spectrum of the sun has been used as an underlying model to remove the illumination components of the spectrum. However, this does not accurately account for the small spectral components present in the solar spectra due to the heavy elements in the sun and molecular compounds in the earth's atmosphere. Attempts to subtract this model from real multi- and hyper-spectral data require further correction depending on the solar spectrum at the time of acquisition since the solar spectrum is a function of its incidence at different times of the day. Complex models of atmospheric particulates and humidity have also been derived to eliminate these background components from spectra. However, these corrective attempts are only models and may not be relevant to the specific conditions when the data was obtained.
A spectrum taken over a wide range of electromagnetic wavelengths may contain several spectral components that are specifically characteristic of a material, such as absorption, transmission, fluorescence, reflection, Raman scatter, etc., as well as, components that may hide these characteristics, e.g., electronic and mechanical instrument noise, background and illumination spectrum. This is especially true with multi-spectral and hyper-spectral imaging in sunlight illumination when analyzing a surface where the reflected illumination and background components are major components that can hide the intrinsic spectral characteristics of the material, particularly fluorescence spectral components.
Hyper-spectral images are produced by simultaneously acquiring numerous images from adjacent narrow wavelength bands and combining them together into a continuous spectrum so as each spatial pixel has its complete related spectrum. The narrow wavelength bands are defined by digital selection from the image's spectrum produced by a grating or prism. Some hyper-spectral cameras use over 250 wavelength bands, whereas, multi-spectral may only use as many as 10 optical filters to produce separate wavelength images that are combined into a continuous spectrum associated with each spatial pixel.
With both the hyper-spectral and multi-spectral images, the illumination components are still present in the resulting spectral image and in the present invention, are considered noise that may be hiding the intrinsic spectral components of the material of interest. This is especially true when trying to detect fluorescence properties where the illumination components overwhelm any emission components. Normally in fluorescent spectroscopy, a single narrow wavelength illumination band is absorbed to excite the material and a long pass barrier filter is used to block out the illumination. Such an approach for hyper-spectral and multi-spectral imaging would be extremely limiting with respect to getting complete spectral information.